Tracking cellular features using motion constraints and global information

Lawrence M. Lifshitz; Fredric S. Fay; Susan Gilbert; Kevin Fogarty; Walter A. Carrington

doi:10.1117/12.34685

1 August 1990 Tracking cellular features using motion constraints and global information

Lawrence M. Lifshitz, Fredric S. Fay, Susan Gilbert, Kevin Fogarty, Walter A. Carrington

Proceedings Volume 1205, Bioimaging and Two-Dimensional Spectroscopy; (1990) https://doi.org/10.1117/12.34685
Event: OE/LASE '90, 1990, Los Angeles, CA, United States

Abstract

This paper discusses research directions and results from a multidisciplinary effort to develop feature extraction tools for analysis of the changes in molecular distribution during cell movement. This work is part of a broader effort directed at developing hardware for a new digital imaging microscope, as well as image restoration algorithms which precede the extraction steps, making them simpler. New voxel based display techniques are also being developed for improved visualization of the two and three dimensional data sets. The most complete feature extraction algorithm developed so far analyzes a time sequence of two-dimensional phase contrast images of newt eosinophilic granulocytes (white blood cells). It tracks a moving cell and also identifies the lamellipods of the cell. This allows the extraction of quantitative information relating cell motility to lamellipod formation. The algorithm finds the cell by finding those pixels in the image which belong to the boundary of the cell . Potential boundary pixels are identified by locating intensity changes due to the phase contrast halo surrounding the cell. While most boundary based image segmentation algorthms form a closed boundary by moving from a starting boundary pixel along a path which locally or globally optimizes a cost function our algorithm does not trace a path from a starting point and does not minimize a cost function. Instead, we close the boundary by examining the geometrical and topological relationships among potential boundary pixels. Gaps in the boundary are closed by connecting gap points to the "closest" boundary point. "Close" is determined by a distance metric which combines Euclidean and other types of geometric information about the boundary pixels already found The position of the cell in the previous image is used both to constrain the location of the cell in the image being examined and to insure that the boundary eventually found is indeed closed.

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Lawrence M. Lifshitz, Fredric S. Fay, Susan Gilbert, Kevin Fogarty, and Walter A. Carrington "Tracking cellular features using motion constraints and global information", Proc. SPIE 1205, Bioimaging and Two-Dimensional Spectroscopy, (1 August 1990); https://doi.org/10.1117/12.34685

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KEYWORDS

Algorithm development

Detection and tracking algorithms

Phase contrast

Spectroscopy

Blood

Image analysis

Analytical research

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